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IT Matters: Optimizing radiation therapy plans with AI

October 02, 2018
Artificial Intelligence
From the October 2018 issue of HealthCare Business News magazine

Babier believes that by perfecting the software by focusing on head and neck, it should translate well to other sites like prostate and breast. The evidence backs him on this, as there have been successful implementations from head and neck to simpler sites in the past. He says the hard part is getting the data to do it.

Regarding the optimization, Babier says the work the software does takes about 20 minutes. For a human to do the same work – to go in and tweak optimization programs and then review – takes 15 to 20 minutes. However, it’s typically not a one-time tweak of a program. Babier says the process of adjusting, reviewing and revising might take a couple of hours to a couple of business days. So if it was “one and done” for a human to do the work, it would be one story, but due to repeat revisions the AI optimization earns its keep.

Babier believes there’s work to be done not just on the software and technical side, but also on the personnel side where the people working on machine learning and those in clinical practice aren’t necessarily speaking the same language, but both sides are starting to understand each other more as the work evolves.

Aaron Babier (Photo Credit Brian Tran)
One of the big takeaways from the work the team did was learning how messy hospital data is. Even within the treatment plans, Babier said there was a lot of information that wasn’t clear on how they were developed. “Even the simplest things, like how certain structures are named, can vary dramatically. Dealing with that weird naming culture is a challenge. The data cleaning work you do upfront is really important to these AI optimization processes. I will say that, apparently, hospitals are getting better with their labeling – I think they’re starting to use scripts or AI more to make it more uniform. When humans are in charge, variation happens very quickly,” Babier said.

The next step for the technology is to get it out to a hospital in Canada to further refine it, with the plan to grow it to other hospitals and other treatment planning software.

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